3 Examples of How Hospitals are Using Predictive Analytics

predictive analyticsPredictive analytics is increasingly key to powering hospital initiatives that maximize efficiency, realize cost savings, and help deliver superior care. Predictive analytics is not new to healthcare, but it is more powerful than ever, due to today’s abundance of data and tools to understand it.

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How – and why – are hospitals putting predictive analytics to work? The goal is often to improve operational efficiency or to proactively provide services that prevent greater problems and spending. Many hospitals have started with applications aimed at reducing readmissions and predicting which patients are at risk of developing sepsis. Other common use cases focus on optimizing staffing and resources. Here are three other examples of hospitals successfully putting predictive analytics into action. (more…)

3 Advantages to Using Simulation in Predictive Analytics

Predictive analytics is a topic generating great hype and great hope in healthcare and other industries. As this area of data science matures, it is important to remember that predictive analytics is not defined by one technology or technique, although it can be roughly divided into two approaches: pattern recognition and simulation.

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Pattern recognition is the most common approach, the foundation of much-hyped machine learning and artificial intelligence. Simulation is another, more human alternative to understanding business problems, predicting future trends, and recommending optimum decisions. In this blog, I explain the essentials of simulation and highlight three of its advantages. (more…)

Why the Time Is Right for Predictive Analytics in Healthcare

Predictive analytics is a technology whose time has come. In healthcare, hospitals are beginning to use advanced analytical techniques to improve patient outcomes and optimize operations with some impressive results.

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Predictive analytics is the process of learning from past data to make predictions about future outcomes. Several factors have set the stage for the emergence of healthcare predictive analytics in 2018 and beyond. In this blog, we will consider three reasons why the time is right. (more…)

Why Collaboration is Key to Enabling the Triangle of Success

Figure 1: The triangle of success

Figure 1: The triangle of success

In previous blog posts, we outlined several of the reasons why business intelligence (BI) projects fail, and we discussed the three sides of our Triangle of Success:

We also outlined the components that comprise each side of our triangle and we discussed the importance of each area as it relates to BI success. We also outlined the pitfalls to avoid during each stage so that your BI project doesn’t fail.

However, even mastering each side of our triangle will not, in and of itself, ensure that your project will be successful. No one component lies in a vacuum. Each one interacts with others during your BI deployment, and what will really determine the success of your implementation is how well you manage these interactions. (more…)

Project Leadership and The Triangle of Success

Figure 1: The triangle of success

Figure 1: The triangle of success

In our three previous blog posts on the Triangle of Success, we discussed why business intelligence (BI) projects often fail and we examined the first two sides of our triangle (Figure 1). To recap, our sides are:

A solid understanding of all three sides of the triangle will put you on the right road to a successful BI implementation. In this post, we will examine the components of our third side, “project leadership,” and discuss the components you need to consider for BI success, as well as the pitfalls you must avoid. (more…)

The Triangle of Success: Understanding Technology

Figure 1: The triangle of success

Figure 1: The triangle of success

In this blog series, we have been discussing the reasons that business intelligence (BI) projects fail and what you need to consider in our Triangle of Success for a successful deployment. To recap, the sides of our triangle (Figure 1) are:

  • Process
  • Technology
  • Project Leadership

We’ve already examined the “process” side of our triangle. In this post, we’ll explore what you need to consider technology-wise to ensure BI success, and examine some of the pitfalls related to technology that could derail your project. (more…)

The Triangle of Success: Understanding Process

Figure 1: The triangle of success

Figure 1: The triangle of success

In our previous blog post, we talked about some of the reasons that business intelligence (BI) projects fail, and we introduced you to the Triangle of Success, which is comprised of three areas that you need to consider to make sure that your BI project is not one of the many that doesn’t live up to expectations. To recap, our sides of the triangle (Figure 1) are:

  • Process
  • Technology
  • Project Leadership

In this blog post, we will discuss the “process” side of our triangle, and explore what you need to examine to ensure BI success. We’ll also look at the pitfalls you need to avoid so that your project doesn’t fail. (more…)

Why BI Projects Fail – And How You Can Avoid Failure through the Triangle of Success

Troubled woman, BI projects failIf you Google “business intelligence failure rate,” you’ll find varying stats on how successful (or rather, unsuccessful) business intelligence (BI) implementations are. One set of statistics estimates that projects fail half the time, while another set estimates they fail a whopping 70 to 80 percent of the time. While the stats vary depending on who is conducting the survey, who they surveyed, and how they define “failure”, the fact remains: BI implementations often fail to meet expectations.

There are myriad reasons that BI projects fail, from inadequately defining the need at the outset to improperly setting and managing expectations during the execution. But generally speaking, we can classify project failures into 4 broad categories: (more…)

BI Collaboration: Shift Emphasis from Insight to Action

Business intelligence collaborationHoward Dresner, noted BI industry analyst, recently hosted a “tweetchat” among his followers in which they discussed “why some organizations haven’t adopted collaborative BI technologies and why some adopted it but failed to achieve the anticipated value.” Howard posed the following question about slow adoption of Collaborative BI to his Twitter followers:

If (Collaborative BI) is so effective, why aren’t organizations jumping on the collaborative bandwagon and why have some not seen the anticipated ROI?

The discussion identified possible reasons ranging from the immaturity of the technology to the cultural gap between established BI practitioners and the younger, social media savvy, “share-all” generation of employees entering the workforce.